Quantifying Organic Material Content in SoilGrids: A Methodological Approach for Layer-wise Percentage Derivation

Decoding SoilGrids: How to Get a Handle on Organic Matter, Layer by Layer

Ever wonder how we know what’s going on beneath our feet, globally speaking? I’m talking about soil – that seemingly simple stuff that’s actually a bustling ecosystem and a key player in everything from growing our food to fighting climate change. One of the most important things about soil is its organic matter (SOM). It’s what makes soil fertile, helps it hold water, and even locks away carbon. So, how do we map this stuff across the planet? That’s where SoilGrids comes in.

Think of SoilGrids as a Google Maps for soil. It’s a global soil mapping system that uses some pretty clever tech to predict what the soil is like in different places. Instead of relying on satellites to spot roads and buildings, SoilGrids uses actual soil samples and environmental data to figure out how much organic carbon is in the ground. And that’s what we’re going to unpack today: how SoilGrids helps us understand organic matter content, layer by layer.

SoilGrids: The Big Picture

Developed by ISRIC—World Soil Information, SoilGrids is more than just a map; it’s a prediction machine. It takes boatloads of soil data, throws in environmental factors like climate and land cover, and then uses machine learning to create a picture of soil properties around the world. What’s really neat is that it gives us this information at different depths – like peeling back the layers of an onion. We get data for six standard depths, from the surface down to two meters! And all this is available at a pretty high resolution, meaning we can zoom in and get a good idea of what’s happening even in relatively small areas. The best part? It’s all open-source, meaning anyone can use it.

How Does SoilGrids Work Its Magic?

So, what’s the secret sauce? Well, it starts with data – mountains of it. SoilGrids uses over 230,000 soil profile observations from around the world, all compiled in the World Soil Information Service (WoSIS) database. Then, it layers on environmental information – things like temperature, rainfall, and even the shape of the land. They sift through over 400 of these environmental factors!

Next comes the brains of the operation: machine learning. SoilGrids uses algorithms like random forest and gradient boosting to find patterns in the data and predict soil properties. It’s like teaching a computer to “read” the landscape and understand what makes soil tick. They don’t just blindly trust the models, though. They put them through rigorous testing to make sure they’re actually accurate.

SoilGrids spits out predictions for all sorts of soil characteristics. We’re talking about things like how much sand, silt, and clay are in the soil, how acidic it is, and even how deep you have to dig before you hit bedrock. But for our purposes, the star of the show is soil organic carbon (SOC).

Turning Carbon into Organic Matter: A Simple Calculation

Here’s where things get really practical. SoilGrids gives us data on soil organic carbon, but we want to know about soil organic matter. So, how do we make that leap? It’s actually a pretty simple conversion.

The rule of thumb is that organic matter is about 58% carbon. So, to get an estimate of organic matter, we multiply the SOC value by a factor of 1.724. It’s like converting inches to centimeters – just a simple multiplication.

SOM (%) = SOC (g/kg) * 1.724 / 10

Let’s say SoilGrids tells us that a particular layer of soil has 10 grams of organic carbon per kilogram of soil. That means we can estimate that it has about 1.724% organic matter. Easy peasy!

The beauty of this is that we can do this calculation for each of the layers that SoilGrids provides. This gives us a picture of how organic matter is distributed throughout the soil profile.

A Word of Caution: SoilGrids Isn’t Perfect

Now, before you go off and make grand pronouncements about global soil health, it’s important to remember that SoilGrids isn’t perfect. It’s a model, and all models have limitations.

Studies have shown that SoilGrids can be pretty accurate in some areas, but less so in others. It all depends on the quality and quantity of data that went into the model. If there’s not much ground-truth data for a particular region, the predictions might not be as reliable.

I’ve seen this firsthand in some of my own research. We were using SoilGrids data to estimate carbon stocks in a remote area, and we found that the predictions were way off compared to our own field measurements. It turned out that the SoilGrids model hadn’t been properly calibrated for that particular type of landscape.

Also, SoilGrids might sometimes overestimate low values for organic carbon content. Plus, the 250-meter resolution might not be fine enough to capture the variability in soil properties in some areas.

The Bottom Line

Despite its limitations, SoilGrids is an incredibly valuable tool. It gives us a way to map soil properties across the globe and understand how they’re changing over time. By understanding how SoilGrids works and being aware of its potential pitfalls, we can use it to make better decisions about land management, agriculture, and climate change. And as SoilGrids continues to evolve and incorporate more data, it will only become more accurate and useful in the years to come. It’s a fascinating project, and it’s helping us unlock the secrets hidden beneath our feet.